PROJECT #18477 RESEARCH FOR INFLUENZA
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

Designed miniproteins are a class of biomolecules with intermediate sizes—larger than small-molecule drugs, but smaller than monoclonal antibodies.

Miniproteins can be computationally designed to tightly bind protein targets for use as potential therapeutics, a promising new avenue for treating infectious disease. Hemagglutinin is a viral fusion protein that allows H1 influenza A (HA) to bind sialic acid on cell surfaces, as well as being involved in the post-endocytosis mechanism of cellular infection.

The Baker lab at University of Washington has developed de novo designed miniproteins that bind hemagglutinin, and improved their binding through affinity maturation (Chevalier et al.

2017).

Many of the mutations seen in affinity-matured sequences are not found in the binding interface, and it remains an open question how these changes lead to higher affinity.

Furthermore, many of the computational predictions of how single-point mutations affect binding deviate significantly from the experimentally determined values. Could all-atom molecular simulation approaches achieve more accurate predictions? In this set of simulations, we aim to use massively parallel expanded ensemble simulations to predict mutational effects on affinities to hemagglutinin.

By pairing these simulations with other simulations aimed at modeling the binding reactions of these miniproteins to hemagglutinin, we aim to have a relatively complete picture of a miniprotein-target binding reaction and how mutations affect it.

These studies are a large-scale investigation on how miniprotein binding reactions work in atomic detail, towards a better understanding of computational design and modulation of miniprotein therapeutics.

PROJECT INFO

Manager(s): Dylan Novack

Institution: Temple University

Project URL: http://voelzlab.org

PROJECT WORK UNIT SUMMARY

Atoms: 93,429

Core: 0xa8

Status: Public

PROJECT FOLDING PPD AVERAGES BY GPU

PPDDB data as of Monday, 20 March 2023 06:14:51

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Project
Model Name
Folding@Home Identifier
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Model
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PROJECT FOLDING PPD AVERAGES BY CPU BETA

PPDDB data as of Monday, 20 March 2023 06:14:51

Rank
Project
CPU Model Logical
Processors (LP)
PPD-PLP
AVG PPD per 1 LP
ALL LP-PPD
(Estimated)
Make
1 RYZEN 7 7700X 8-CORE 16 37,407 598,512 AMD
2 RYZEN 9 5950X 16-CORE 32 14,012 448,384 AMD
3 11TH GEN CORE I7-11700K @ 3.60GHZ 16 26,591 425,456 Intel
4 RYZEN 7 5700X 8-CORE 16 26,076 417,216 AMD
5 RYZEN 9 5900X 12-CORE 24 14,856 356,544 AMD
6 RYZEN 7 5800X 8-CORE 16 20,388 326,208 AMD
7 RYZEN 7 5800X3D 8-CORE 16 20,143 322,288 AMD
8 XEON PLATINUM 8370C CPU @ 2.80GHZ 16 19,323 309,168 Intel
9 12TH GEN CORE I7-12700 20 14,361 287,220 Intel
10 RYZEN 9 3900X 12-CORE 24 11,844 284,256 AMD
11 CORE I7-10700K CPU @ 3.80GHZ 16 16,128 258,048 Intel
12 RYZEN 7 3700X 8-CORE 16 14,095 225,520 AMD
13 CORE I7-10700T CPU @ 2.00GHZ 16 5,589 89,424 Intel
14 XEON CPU L5640 @ 2.27GHZ 24 2,439 58,536 Intel